Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

Neural approaches have achieved state-of-the-art accuracy on machine translation but
suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has …

This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP
for short) for Neural Machine Translation (NMT). Unlike traditional pre-training method which …

This paper studies the practicality of the current state-of-the-art unsupervised methods in
neural machine translation (NMT). In ten translation tasks with various data settings, we …

A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon
which generates a response, which usually faces the problem of error propagation from both …

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results
for several language pairs. However, it can only translate between a single language pair …

In this paper, we propose a new task of machine translation (MT), which is based on no
parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the ability …

Neural Machine Translation (NMT) brings promising improvements in translation quality, but
until recently, these models rely on large-scale parallel corpora. As such corpora only exist …